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Can we train a single transformer model capable of processing multiple modalities and datasets, whilst sharing almost all of its learnable parameters? We present PolyViT, a model trained on image, audio and video which answers this question.
Multitask learning
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The kinetics human action video dataset
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Non-local neural networks
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Video modeling with correlation networks
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Attentionnas: Spatiotemporal attention cell search for video classification
Xiaofang Wang, Xuehan Xiong, Maxim Neumann, AJ Piergiovanni, Michael S Ryoo, Anelia Angelova, Kris M Kitani, and Wei Hua · 2020
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Gradient surgery for multi-task learning
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Vatt: Transformers for multimodal self-supervised learning from raw video, audio and text
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Frozen in time: A joint video and image encoder for end-to-end retrieval
Max Bain, Arsha Nagrani, Gül Varol, and Andrew Zisserman · 2021
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Is space-time attention all you need for video understanding?
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An image is worth 16x16 words: Transformers for image recognition at scale
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Haoqi Fan, Bo Xiong, Karttikeya Mangalam, Yanghao Li, Zhicheng Yan, Jitendra Malik, and Christoph Feichtenhofer · 2021
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AST: Audio Spectrogram Transformer
Yuan Gong, Yu-An Chung, and James Glass · 2021
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Unit: Multimodal multitask learning with a unified transformer
Ronghang Hu and Amanpreet Singh · 2021
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Perceiver: General perception with iterative attention
Andrew Jaegle, Felix Gimeno, Andrew Brock, Andrew Zisserman, Oriol Vinyals, and Joao Carreira · 2021
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Perceiver: General perception with iterative attention, 2021
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Slow-fast auditory streams for audio recognition
Evangelos Kazakos, Arsha Nagrani, Andrew Zisserman, and Dima Damen · 2021
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Parameter efficient multimodal transformers for video representation learning
Sangho Lee, Youngjae Yu, Gunhee Kim, Thomas Breuel, Jan Kautz, and Yale Song · 2021
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Attention bottlenecks for multimodal fusion
Arsha Nagrani, Shan Yang, Anurag Arnab, Aren Jansen, Cordelia Schmid, and Chen Sun · 2021
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Keeping your eye on the ball: Trajectory attention in video transformers
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